On the Spatial and Temporal Influence for the Reconstruction of Magnetic Resonance Fingerprinting

Fabian Balsiger, Olivier Scheidegger, Pierre G. Carlier, Benjamin Marty, Mauricio Reyes
Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning, PMLR 102:27-38, 2019.

Abstract

Magnetic resonance fingerprinting (MRF) is a promising tool for fast and multiparametric quantitative MR imaging. A drawback of MRF, however, is that the reconstruction of the MR maps is computationally demanding and lacks scalability. Several works have been proposed to improve the reconstruction of MRF by deep learning methods. Unfortunately, such methods have never been evaluated on an extensive clinical data set, and there exists no consensus on whether a fingerprint-wise or spatiotemporal reconstruction is favorable. Therefore, we propose a convolutional neural network (CNN) that reconstructs MR maps from MRF-WF, a MRF sequence for neuromuscular diseases. We evaluated the CNN’s performance on a large and highly heterogeneous data set consisting of 95 patients with various neuromuscular diseases. We empirically show the benefit of using the information of neighboring fingerprints and visualize, via occlusion experiments, the importance of temporal frames for the reconstruction.

Cite this Paper

BibTeX
@InProceedings{pmlr-v102-balsiger19a, title = {On the Spatial and Temporal Influence for the Reconstruction of Magnetic Resonance Fingerprinting}, author = {Balsiger, Fabian and Scheidegger, Olivier and Carlier, Pierre G. and Marty, Benjamin and Reyes, Mauricio}, booktitle = {Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning}, pages = {27--38}, year = {2019}, editor = {Cardoso, M. Jorge and Feragen, Aasa and Glocker, Ben and Konukoglu, Ender and Oguz, Ipek and Unal, Gozde and Vercauteren, Tom}, volume = {102}, series = {Proceedings of Machine Learning Research}, month = {08--10 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v102/balsiger19a/balsiger19a.pdf}, url = {https://proceedings.mlr.press/v102/balsiger19a.html}, abstract = {Magnetic resonance fingerprinting (MRF) is a promising tool for fast and multiparametric quantitative MR imaging. A drawback of MRF, however, is that the reconstruction of the MR maps is computationally demanding and lacks scalability. Several works have been proposed to improve the reconstruction of MRF by deep learning methods. Unfortunately, such methods have never been evaluated on an extensive clinical data set, and there exists no consensus on whether a fingerprint-wise or spatiotemporal reconstruction is favorable. Therefore, we propose a convolutional neural network (CNN) that reconstructs MR maps from MRF-WF, a MRF sequence for neuromuscular diseases. We evaluated the CNN’s performance on a large and highly heterogeneous data set consisting of 95 patients with various neuromuscular diseases. We empirically show the benefit of using the information of neighboring fingerprints and visualize, via occlusion experiments, the importance of temporal frames for the reconstruction.} }
Endnote
%0 Conference Paper %T On the Spatial and Temporal Influence for the Reconstruction of Magnetic Resonance Fingerprinting %A Fabian Balsiger %A Olivier Scheidegger %A Pierre G. Carlier %A Benjamin Marty %A Mauricio Reyes %B Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2019 %E M. Jorge Cardoso %E Aasa Feragen %E Ben Glocker %E Ender Konukoglu %E Ipek Oguz %E Gozde Unal %E Tom Vercauteren %F pmlr-v102-balsiger19a %I PMLR %P 27--38 %U https://proceedings.mlr.press/v102/balsiger19a.html %V 102 %X Magnetic resonance fingerprinting (MRF) is a promising tool for fast and multiparametric quantitative MR imaging. A drawback of MRF, however, is that the reconstruction of the MR maps is computationally demanding and lacks scalability. Several works have been proposed to improve the reconstruction of MRF by deep learning methods. Unfortunately, such methods have never been evaluated on an extensive clinical data set, and there exists no consensus on whether a fingerprint-wise or spatiotemporal reconstruction is favorable. Therefore, we propose a convolutional neural network (CNN) that reconstructs MR maps from MRF-WF, a MRF sequence for neuromuscular diseases. We evaluated the CNN’s performance on a large and highly heterogeneous data set consisting of 95 patients with various neuromuscular diseases. We empirically show the benefit of using the information of neighboring fingerprints and visualize, via occlusion experiments, the importance of temporal frames for the reconstruction.
APA
Balsiger, F., Scheidegger, O., Carlier, P.G., Marty, B. & Reyes, M.. (2019). On the Spatial and Temporal Influence for the Reconstruction of Magnetic Resonance Fingerprinting. Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 102:27-38 Available from https://proceedings.mlr.press/v102/balsiger19a.html.

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